Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach

The Application of Bayesian Methods in Cancer Prognosis and Prediction

With the development of high-throughput biological techniques, high-dimensional omics data have emerged. These molecular data provide a solid foundation for precision medicine and prognostic prediction of cancer. Bayesian methods contribute to constructing prognostic models with complex relationships in omics and improving performance by introducing different prior distribution, which is suitable for modelling the high-dimensional data involved. Using different omics, several Bayesian hierarchical approaches have been proposed for variable selection and model construction. In particular, the Bayesian methods of multi-omics integration have also been consistently proposed in recent years. Compared with single-omics, multi-omics integration modelling will contribute to improving predictive performance, gaining insights into the underlying mechanisms of tumour occurrence and development, and the discovery of more reliable biomarkers. In this work, we present a review of current proposed Bayesian approaches in prognostic prediction modelling in cancer.

Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data

BACKGROUND

The endogenous circadian clock, which controls daily rhythms in the expression of at least half of the mammalian genome, has a major influence on cell physiology. Consequently, disruption of the circadian system is associated with wide range of diseases including cancer. While several circadian clock genes have been associated with cancer progression, little is known about the survival when two or more platforms are considered together. Our goal was to determine if survival outcomes are associated with circadian clock function. To accomplish this goal, we developed a Bayesian hierarchical survival model coupled with the global local shrinkage prior and applied this model to available RNASeq and Copy Number Variation data to select significant circadian genes associates with cancer progression.

RESULTS

Using a Bayesian shrinkage approach with the Bayesian accelerated failure time (AFT) model we showed the circadian clock associated gene DEC1 is positively correlated to survival outcome in breast cancer patients. The R package circgene implementing the methodology is available at https://github.com/MAITYA02/circgene.

CONCLUSIONS

The proposed Bayesian hierarchical model is the first shrinkage prior based model in its kind which integrates two omics platforms to identify the significant circadian gene for cancer survival.